





"""simple docstring"""





import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dataset				,		SCREAMING_SNAKE_CASE	:		Dict[str, str]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			args.log_outputs
      lowerCAmelCase    		=			"""_""".join(args.dataset.split("""/"""     ) + [args.config, args.split]     )

      # load metric
      lowerCAmelCase    		=			load_metric("""wer"""     )
      lowerCAmelCase    		=			load_metric("""cer"""     )

      # compute metrics
      lowerCAmelCase    		=			wer.compute(references=result["""target"""]				,		predictions=result["""prediction"""]     )
      lowerCAmelCase    		=			cer.compute(references=result["""target"""]				,		predictions=result["""prediction"""]     )

      # print & log results
      lowerCAmelCase    		=			F'WER: {wer_result}\nCER: {cer_result}'
      print(SCREAMING_SNAKE_CASE     )

      with open(F'{dataset_id}_eval_results.txt'				,		"""w"""     ) as f:
            f.write(SCREAMING_SNAKE_CASE     )

      # log all results in text file. Possibly interesting for analysis
      if log_outputs is not None:
            lowerCAmelCase    		=			F'log_{dataset_id}_predictions.txt'
            lowerCAmelCase    		=			F'log_{dataset_id}_targets.txt'

            with open(SCREAMING_SNAKE_CASE				,		"""w"""     ) as p, open(SCREAMING_SNAKE_CASE				,		"""w"""     ) as t:
                  # mapping function to write output
                  def write_to_file(SCREAMING_SNAKE_CASE	:		Union[str, Any]				,		SCREAMING_SNAKE_CASE	:		Any     ):
                        p.write(F'{i}' + """\n"""     )
                        p.write(batch["""prediction"""] + """\n"""     )
                        t.write(F'{i}' + """\n"""     )
                        t.write(batch["""target"""] + """\n"""     )

                  result.map(SCREAMING_SNAKE_CASE				,		with_indices=SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

      lowerCAmelCase    		=			re.sub(SCREAMING_SNAKE_CASE				,		""""""				,		text.lower()     )

      # In addition, we can normalize the target text, e.g. removing new lines characters etc...
      # note that order is important here!
      lowerCAmelCase    		=			["""\n\n""", """\n""", """   """, """  """]

      for t in token_sequences_to_ignore:
            lowerCAmelCase    		=			""" """.join(text.split(SCREAMING_SNAKE_CASE     )     )

      return text


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			load_dataset(args.dataset				,		args.config				,		split=args.split				,		use_auth_token=SCREAMING_SNAKE_CASE     )

      # for testing: only process the first two examples as a test
      # dataset = dataset.select(range(10))

      # load processor
      lowerCAmelCase    		=			AutoFeatureExtractor.from_pretrained(args.model_id     )
      lowerCAmelCase    		=			feature_extractor.sampling_rate

      # resample audio
      lowerCAmelCase    		=			dataset.cast_column("""audio"""				,		Audio(sampling_rate=SCREAMING_SNAKE_CASE     )     )

      # load eval pipeline
      if args.device is None:
            lowerCAmelCase    		=			0 if torch.cuda.is_available() else -1
      lowerCAmelCase    		=			pipeline("""automatic-speech-recognition"""				,		model=args.model_id				,		device=args.device     )

      # map function to decode audio
      def map_to_pred(SCREAMING_SNAKE_CASE	:		List[str]     ):
            lowerCAmelCase    		=			asr(
                batch["""audio"""]["""array"""]				,		chunk_length_s=args.chunk_length_s				,		stride_length_s=args.stride_length_s     )

            lowerCAmelCase    		=			prediction["""text"""]
            lowerCAmelCase    		=			normalize_text(batch["""sentence"""]     )
            return batch

      # run inference on all examples
      lowerCAmelCase    		=			dataset.map(SCREAMING_SNAKE_CASE				,		remove_columns=dataset.column_names     )

      # compute and log_results
      # do not change function below
      log_results(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()

       parser.add_argument(
           "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
       )
       parser.add_argument(
           "--dataset",
           type=str,
           required=True,
           help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
       )
       parser.add_argument(
           "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
       )
       parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
       parser.add_argument(
           "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
       )
       parser.add_argument(
           "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
       )
       parser.add_argument(
           "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
       )
       parser.add_argument(
           "--device",
           type=int,
           default=None,
           help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
       )
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()

       main(args)













"""simple docstring"""





import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

SCREAMING_SNAKE_CASE__   =  [
    "cross_validation.py",
    "gradient_accumulation.py",
    "local_sgd.py",
    "multi_process_metrics.py",
    "memory.py",
    "automatic_gradient_accumulation.py",
    "fsdp_with_peak_mem_tracking.py",
    "deepspeed_with_config_support.py",
    "megatron_lm_gpt_pretraining.py",
]

class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = None ,		lowercase = None					)						->	Dict:
            lowerCAmelCase    		=			None
            lowerCAmelCase    		=			os.path.abspath(os.path.join("""examples""" ,		"""by_feature"""					)					)
            lowerCAmelCase    		=			os.path.abspath("""examples"""					)
            for item in os.listdir(lowercase					):
                  if item not in EXCLUDE_EXAMPLES:
                        lowerCAmelCase    		=			os.path.join(lowercase ,		lowercase					)
                        if os.path.isfile(lowercase					) and ".py" in item_path:
                              with self.subTest(
                                  tested_script=lowercase ,		feature_script=lowercase ,		tested_section="""main()""" if parser_only else """training_function()""" ,		):
                                    lowerCAmelCase    		=			compare_against_test(
                                        os.path.join(lowercase ,		lowercase					) ,		lowercase ,		lowercase ,		lowercase					)
                                    lowerCAmelCase    		=			"""\n""".join(lowercase					)
                                    if special_strings is not None:
                                          for string in special_strings:
                                                lowerCAmelCase    		=			diff.replace(lowercase ,		""""""					)
                                    self.assertEqual(lowercase ,		""""""					)





      def        _snake_case     (			self					)						->	List[Any]:
            self.one_complete_example("""complete_nlp_example.py""" ,		lowercase					)
            self.one_complete_example("""complete_nlp_example.py""" ,		lowercase					)





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			os.path.abspath(os.path.join("""examples""" ,		"""cv_example.py"""					)					)
            lowerCAmelCase    		=			[
                """ """ * 16 + """{\n\n""",
                """ """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
                """ """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
                """ """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
                """ """ * 20 + """\"epoch\": epoch,\n\n""",
                """ """ * 16 + """},\n\n""",
                """ """ * 16 + """step=epoch,\n""",
                """ """ * 12,
                """ """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
            ]
            self.one_complete_example("""complete_cv_example.py""" ,		lowercase ,		lowercase ,		lowercase					)
            self.one_complete_example("""complete_cv_example.py""" ,		lowercase ,		lowercase ,		lowercase					)







@mock.patch.dict(os.environ	,    {'TESTING_MOCKED_DATALOADERS': '1'}   )
class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = False





      @classmethod
      def        _snake_case     (			cls					)						->	Optional[int]:
            super().setUpClass()
            lowerCAmelCase    		=			tempfile.mkdtemp()
            lowerCAmelCase    		=			os.path.join(cls._tmpdir ,		"""default_config.yml"""					)

            write_basic_config(save_location=cls.configPath					)
            lowerCAmelCase    		=			["""accelerate""", """launch""", """--config_file""", cls.configPath]





      @classmethod
      def        _snake_case     (			cls					)						->	Optional[int]:
            super().tearDownClass()
            shutil.rmtree(cls._tmpdir					)





      def        _snake_case     (			self					)						->	str:
            lowerCAmelCase    		=			f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        '.split()
            run_command(self._launch_args + testargs					)
            self.assertTrue(os.path.exists(os.path.join(self.tmpdir ,		"""epoch_0"""					)					)					)





      def        _snake_case     (			self					)						->	Optional[int]:
            lowerCAmelCase    		=			f'\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        '.split()
            lowerCAmelCase    		=			run_command(self._launch_args + testargs					)
            self.assertTrue(os.path.exists(os.path.join(self.tmpdir ,		"""step_2"""					)					)					)





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir ,		"epoch_0"					)}\n        '.split()
            lowerCAmelCase    		=			run_command(self._launch_args + testargs ,		return_stdout=lowercase					)
            self.assertNotIn("""epoch 0:""" ,		lowercase					)
            self.assertIn("""epoch 1:""" ,		lowercase					)





      def        _snake_case     (			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			f'\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir ,		"step_2"					)}\n        '.split()
            lowerCAmelCase    		=			run_command(self._launch_args + testargs ,		return_stdout=lowercase					)
            if torch.cuda.is_available():
                  lowerCAmelCase    		=			torch.cuda.device_count()
            else:
                  lowerCAmelCase    		=			1
            if num_processes > 1:
                  self.assertNotIn("""epoch 0:""" ,		lowercase					)
                  self.assertIn("""epoch 1:""" ,		lowercase					)
            else:
                  self.assertIn("""epoch 0:""" ,		lowercase					)
                  self.assertIn("""epoch 1:""" ,		lowercase					)





      @slow
      def        _snake_case     (			self					)						->	Tuple:
            lowerCAmelCase    		=			"""
        examples/by_feature/cross_validation.py
        --num_folds 2
        """.split()
            with mock.patch.dict(os.environ ,		{"""TESTING_MOCKED_DATALOADERS""": """0"""}					):
                  lowerCAmelCase    		=			run_command(self._launch_args + testargs ,		return_stdout=lowercase					)
                  lowerCAmelCase    		=			re.findall("""({.+})""" ,		lowercase					)
                  lowerCAmelCase    		=			[r for r in results if """accuracy""" in r][-1]
                  lowerCAmelCase    		=			ast.literal_eval(lowercase					)
                  self.assertGreaterEqual(results["""accuracy"""] ,		0.75					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			["""examples/by_feature/multi_process_metrics.py"""]
            run_command(self._launch_args + testargs					)





      @require_trackers
      @mock.patch.dict(os.environ ,		{"""WANDB_MODE""": """offline"""}					)
      def        _snake_case     (			self					)						->	Any:
            with tempfile.TemporaryDirectory() as tmpdir:
                  lowerCAmelCase    		=			f'\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            '.split()
                  run_command(self._launch_args + testargs					)
                  self.assertTrue(os.path.exists(os.path.join(lowercase ,		"""tracking"""					)					)					)





      def        _snake_case     (			self					)						->	Union[str, Any]:
            lowerCAmelCase    		=			["""examples/by_feature/gradient_accumulation.py"""]
            run_command(self._launch_args + testargs					)





      def        _snake_case     (			self					)						->	int:
            lowerCAmelCase    		=			["""examples/by_feature/local_sgd.py"""]
            run_command(self._launch_args + testargs					)






